Interactive Data

Overall Heat Map

We generated a heat map using the Leaflet library to visualize the geographical distribution of rat sightings based on latitude and longitude.

top = 40.917577 # north lat
left = -74.259090 # west long
right = -73.700272 # east long
bottom =  40.477399 # south lat


nyc = rats_raw %>%
  filter(latitude >= bottom) %>%
  filter ( latitude <= top) %>%
  filter( longitude >= left ) %>%
  filter(longitude <= right)

center_lon = median(nyc$longitude,na.rm = TRUE)
center_lat = median(nyc$latitude,na.rm = TRUE)

count = nyc %>%
  group_by(location) %>%
  count()

nyc = merge(nyc, count, by = "location")

factpal = colorFactor("blue", nyc$n)

nyc %>%
  leaflet() %>%
  addProviderTiles("Esri.NatGeoWorldMap") %>%
  addHeatmap(lng = ~longitude, lat = ~latitude, intensity = ~(nyc$n), blur = 20, max = 0.05, radius = 15) %>%
  setView(lng=center_lon, lat=center_lat,zoom = 10)

In summary, the heat map visualizes the density of occurrences of rat sightings within NYC. Warmer colors indicate higher density, while cooler colors represent lower density.

Overall Time Series Plot

overall <- rats_raw %>% 
  group_by(sighting_year, sighting_month_num, sighting_day) %>% 
  summarize(count = n()) %>% 
  mutate(date = as.Date(paste(sighting_year, sighting_month_num, sighting_day, sep = "-")))

time_series = xts(overall$count , order.by= overall$date)

hchart(time_series, name = "Rat Sightings") %>% 
  hc_add_theme(hc_theme_darkunica()) %>%
  hc_credits(enabled = TRUE, text = "Sources: City of New York", style = list(fontSize = "12px")) %>%
  hc_title(text = "Time Series of NYC Rat Sightings") %>%
  hc_legend(enabled = TRUE)

The time series plot visually represents the trend and pattern of rat sightings in New York City over time. The x-axis represents the timeline, and the y-axis represents the count of rat sightings on each corresponding date.